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Modelling volatility with mixture density networks

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2008
Type
Conference Paper

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Abstract

Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models.

Citation
Mostafa, Fahed and Dillon, Tharam. 2008. Modelling volatility with mixture density networks, in Hu, X.T. and Liu, Q. (ed), IEEE International Conference on Granular Computing, Aug 26 2008, pp. 501-505. Hangzhou, China: Institute of Electrical and Electronics Engineers (IEEE).
Source Title
Proceedings of the IEEE international conference on granular computing (GrC 2008)
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Faculty
Curtin Business School
School of Economics and Finance
School
Centre for Extended Enterprises and Business Intelligence
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